no code implementations • 16 Aug 2023 • Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
However, they are constrained to generate proteins with only the set of amino acids represented in their vocabulary.
no code implementations • 9 May 2023 • Daniel Flam-Shepherd, Alán Aspuru-Guzik
In doing so, we demonstrate that it is not necessary to use simplified molecular representations to train chemical language models -- that they are powerful generative models capable of directly exploring chemical space in three dimensions for very different structures.
no code implementations • 1 Feb 2022 • Daniel Flam-Shepherd, Alexander Zhigalin, Alán Aspuru-Guzik
We introduce a novel RL framework for scalable 3D design that uses a hierarchical agent to build molecules by placing molecular substructures sequentially in 3D space, thus attempting to build on the existing human knowledge in the field of molecular design.
1 code implementation • 6 Dec 2021 • Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
In this work, we investigate the capacity of simple language models to learn distributions of molecules.
1 code implementation • 20 Oct 2021 • Matthew Choi, Daniel Flam-Shepherd, Thi Ha Kyaw, Alán Aspuru-Guzik
The core objective of machine-assisted scientific discovery is to learn physical laws from experimental data without prior knowledge of the systems in question.
1 code implementation • 6 Sep 2021 • Daniel Flam-Shepherd, Tony Wu, Xuemei Gu, Alba Cervera-Lierta, Mario Krenn, Alan Aspuru-Guzik
The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand.
no code implementations • 3 Nov 2020 • Tony C. Wu, Daniel Flam-Shepherd, Alán Aspuru-Guzik
This paper focuses on Bayesian Optimization in combinatorial spaces.
no code implementations • 24 Feb 2020 • Daniel Flam-Shepherd, Tony Wu, Pascal Friederich, Alan Aspuru-Guzik
Graph neural network have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures in the graph such as functional groups and molecular geometry.
no code implementations • 14 Feb 2020 • Daniel Flam-Shepherd, Tony Wu, Alan Aspuru-Guzik
Graph generation is an extremely important task, as graphs are found throughout different areas of science and engineering.